Skip to main content

Recent Advances in Logo Detection Using Machine Learning Paradigms

Theory and Practice

  • Book
  • © 2024

Overview

  • Presents the novel logo detection methods using machine learning paradigms
  • Demonstrates the merits of the presented approaches over the reported approaches using the real-world applications
  • ​ Includes the state-of-the-art machine learning paradigms

Part of the book series: Intelligent Systems Reference Library (ISRL, volume 255)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.

This book provides numerous ways that deep learners can use for logo recognition, including:

  • Deep learning-based end-to-end trainable architecture for logo detection
  • Weakly supervised logo recognition approach using attention mechanisms
  • Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images
  • Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images
  • Approach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.

The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.

The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.

 

 

 

Keywords

Table of contents (6 chapters)

Authors and Affiliations

  • College of Information Science and Engineering, Ritsumeikan University, Kusuatsu, Japan

    Yen-Wei Chen

  • Founder & CEO, Tiwaki Co., Ltd, Kusatsu, Japan

    Xiang Ruan

  • College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan

    Rahul Kumar Jain

About the authors



Bibliographic Information

  • Book Title: Recent Advances in Logo Detection Using Machine Learning Paradigms

  • Book Subtitle: Theory and Practice

  • Authors: Yen-Wei Chen, Xiang Ruan, Rahul Kumar Jain

  • Series Title: Intelligent Systems Reference Library

  • DOI: https://doi.org/10.1007/978-3-031-59811-1

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024

  • Hardcover ISBN: 978-3-031-59810-4Published: 31 May 2024

  • Softcover ISBN: 978-3-031-59813-5Published: 01 June 2025

  • eBook ISBN: 978-3-031-59811-1Published: 30 May 2024

  • Series ISSN: 1868-4394

  • Series E-ISSN: 1868-4408

  • Edition Number: 1

  • Number of Pages: XII, 119

  • Number of Illustrations: 1 b/w illustrations, 63 illustrations in colour

  • Topics: Data Engineering, Computational Intelligence, Artificial Intelligence

Publish with us